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Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition

Author

Listed:
  • Pan Fan

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    School of Computer Science and Technology, Baoji University of Arts and Science, Baoji 721016, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China)

  • Guodong Lang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China)

  • Pengju Guo

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China
    State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling 712100, China)

  • Zhijie Liu

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China
    State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling 712100, China)

  • Fuzeng Yang

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China
    State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling 712100, China)

  • Bin Yan

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China
    State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling 712100, China)

  • Xiaoyan Lei

    (College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China
    Shannxi Key Laboratory of Apple, Yangling 712100, China
    Apple Mechanized Research Base, Yangling 712100, China
    State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling 712100, China)

Abstract

In the vision system of apple-picking robots, the main challenge is to rapidly and accurately identify the apple targets with varying halation and shadows on their surfaces. To solve this problem, this study proposes a novel, multi-feature, patch-based apple image segmentation technique using the gray-centered red-green-blue (RGB) color space. The developed method presents a multi-feature selection process, which eliminates the effect of halation and shadows in apple images. By exploring all the features of the image, including halation and shadows, in the gray-centered RGB color space, the proposed algorithm, which is a generalization of K-means clustering algorithm, provides an efficient target segmentation result. The proposed method is tested on 240 apple images. It offered an average accuracy rate of 98.79%, a recall rate of 99.91%, an F1 measure of 99.35%, a false positive rate of 0.04%, and a false negative rate of 1.18%. Compared with the classical segmentation methods and conventional clustering algorithms, as well as the popular deep-learning segmentation algorithms, the proposed method can perform with high efficiency and accuracy to guide robotic harvesting.

Suggested Citation

  • Pan Fan & Guodong Lang & Pengju Guo & Zhijie Liu & Fuzeng Yang & Bin Yan & Xiaoyan Lei, 2021. "Multi-Feature Patch-Based Segmentation Technique in the Gray-Centered RGB Color Space for Improved Apple Target Recognition," Agriculture, MDPI, vol. 11(3), pages 1-18, March.
  • Handle: RePEc:gam:jagris:v:11:y:2021:i:3:p:273-:d:521964
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    References listed on IDEAS

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    1. Sajad Sabzi & Yousef Abbaspour-Gilandeh & Jose Luis Hernandez-Hernandez & Farzad Azadshahraki & Rouhollah Karimzadeh, 2019. "The Use of the Combination of Texture, Color and Intensity Transformation Features for Segmentation in the Outdoors with Emphasis on Video Processing," Agriculture, MDPI, vol. 9(5), pages 1-14, May.
    2. Ewa Ropelewska, 2020. "The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp," Agriculture, MDPI, vol. 11(1), pages 1-12, December.
    3. Ting Yuan & Lin Lv & Fan Zhang & Jun Fu & Jin Gao & Junxiong Zhang & Wei Li & Chunlong Zhang & Wenqiang Zhang, 2020. "Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD," Agriculture, MDPI, vol. 10(5), pages 1-14, May.
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    Cited by:

    1. Huawei Yang & Yinzeng Liu & Shaowei Wang & Huixing Qu & Ning Li & Jie Wu & Yinfa Yan & Hongjian Zhang & Jinxing Wang & Jianfeng Qiu, 2023. "Improved Apple Fruit Target Recognition Method Based on YOLOv7 Model," Agriculture, MDPI, vol. 13(7), pages 1-21, June.
    2. Ruilong Gao & Qiaojun Zhou & Songxiao Cao & Qing Jiang, 2022. "An Algorithm for Calculating Apple Picking Direction Based on 3D Vision," Agriculture, MDPI, vol. 12(8), pages 1-19, August.

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